Search Results for author: Wonyong Jeong

Found 7 papers, 4 papers with code

Personalized Subgraph Federated Learning

1 code implementation21 Jun 2022 Jinheon Baek, Wonyong Jeong, Jiongdao Jin, Jaehong Yoon, Sung Ju Hwang

To this end, we introduce a new subgraph FL problem, personalized subgraph FL, which focuses on the joint improvement of the interrelated local GNNs rather than learning a single global model, and propose a novel framework, FEDerated Personalized sUBgraph learning (FED-PUB), to tackle it.

Federated Learning

Bitwidth Heterogeneous Federated Learning with Progressive Weight Dequantization

no code implementations23 Feb 2022 Jaehong Yoon, Geon Park, Wonyong Jeong, Sung Ju Hwang

We introduce a pragmatic FL scenario with bitwidth heterogeneity across the participating devices, dubbed as Bitwidth Heterogeneous Federated Learning (BHFL).

Federated Learning

Factorized-FL: Agnostic Personalized Federated Learning with Kernel Factorization & Similarity Matching

no code implementations1 Feb 2022 Wonyong Jeong, Sung Ju Hwang

In real-world federated learning scenarios, participants could have their own personalized labels which are incompatible with those from other clients, due to using different label permutations or tackling completely different tasks or domains.

Personalized Federated Learning

Agnostic Personalized Federated Learning with Kernel Factorization

no code implementations29 Sep 2021 Wonyong Jeong, Sung Ju Hwang

We then study two essential challenges of the agnostic personalized federated learning, which are (1) Label Heterogeneity where local clients learn from the same single domain but labeling schemes are not synchronized with each other and (2) Domain Heterogeneity where the clients learn from the different datasets which can be semantically similar or dissimilar for each other.

Personalized Federated Learning Semantic Similarity +1

Task-Adaptive Neural Network Search with Meta-Contrastive Learning

1 code implementation NeurIPS 2021 Wonyong Jeong, Hayeon Lee, Gun Park, Eunyoung Hyung, Jinheon Baek, Sung Ju Hwang

To address such limitations, we introduce a novel problem of \emph{Neural Network Search} (NNS), whose goal is to search for the optimal pretrained network for a novel dataset and constraints (e. g. number of parameters), from a model zoo.

Contrastive Learning Meta-Learning +1

Federated Semi-Supervised Learning with Inter-Client Consistency & Disjoint Learning

1 code implementation ICLR 2021 Wonyong Jeong, Jaehong Yoon, Eunho Yang, Sung Ju Hwang

Through extensive experimental validation of our method in the two different scenarios, we show that our method outperforms both local semi-supervised learning and baselines which naively combine federated learning with semi-supervised learning.

Federated Learning

Federated Continual Learning with Weighted Inter-client Transfer

1 code implementation6 Mar 2020 Jaehong Yoon, Wonyong Jeong, Giwoong Lee, Eunho Yang, Sung Ju Hwang

There has been a surge of interest in continual learning and federated learning, both of which are important in deep neural networks in real-world scenarios.

Continual Learning Federated Learning +1

Cannot find the paper you are looking for? You can Submit a new open access paper.